Seoul National University College of Engineering announced that a research team led by Professor Yong-Lae Park from the Department of Mechanical Engineering has developed an “intelligent artificial muscle” capable of simultaneously performing sensing and actuation functions, inspired by biological muscle–tendon complexes.

This artificial muscle, which embeds liquid metal channels within a liquid crystal elastomer (LCE), contracts in response to electrical stimulation while also being able to measure internal force and length in real time. As a result, it enables the simultaneous processing of motor signals (somatic nervous system) and sensory signals (sensory nervous system), similar to biological muscles. The technology is regarded as a breakthrough with strong potential for application in next-generation humanoid robots.

The research findings were published in the prestigious international journal Advanced Materials and were also selected as a cover article.

AI TO EYE: Between Code and Conscience offers a concise and vivid portrait of how artificial intelligence is reshaping contemporary society. In addition to insights drawn from literature and the wider cultural record, 14 short essays and a rich mosaic of voices from more than 40 interviews unite perspectives on this topic from science, industry, journalism, film, music, and the arts. Rather than explaining AI in technical terms, this volume presents a human-centered view of how people across disciplines experience the ongoing debate and apply this powerful technology. From education and media to healthcare and the creative industries, the contributors illuminate both the emerging opportunities and the challenges of an AI-driven world. AI TO EYE blends contemporary conversations with enduring ideas from culture and intellectual history to deliver a polyphonic and highly quotable cultural document that meets AI “eye to eye.”

This paper systematically reviews the research progress and application of machine learning in adsorption processes. By virtue of outstanding nonlinear modeling and data mining capabilities, machine learning has been successfully applied to adsorbent design and high-throughput screening, adsorption parameter prediction and process optimization, reactor design and digital twin simulation, as well as interpretable model development. The reviewed studies demonstrate that machine learning enables highly accurate prediction of adsorption performance, greatly accelerates material discovery and process optimization, and significantly reduces experimental and computational costs. This work also summarizes mainstream machine learning models, public adsorption databases, common simulation methods, and current challenges including insufficient model interpretability, data scarcity, and insufficient coupling with physical mechanisms. Finally, future directions such as deep integration of mechanistic models and AI, intelligent adsorption 2.0, and AI-assisted 3D printing for reactor manufacturing are prospected. This review is expected to provide a comprehensive reference for promoting the intelligent, efficient, and green development of adsorption separation technology.

For the first time, researchers have shown through simulation that a novel X-shaped sleeper enhances trackbed stability under impact loading by redistributing loads through a denser, more uniform network of force chains, reducing stress concentrations and improving energy dissipation.

Conventional soft actuators are often limited by weak force, small displacement, and slow response. To overcome these limitations, researchers have developed a new mechanical system that can amplify motion and remember external triggers through the interaction between magnets and elastic membranes.

Seoul National University College of Engineering announced that a joint research team led by Prof. Jeong-Yun Sun from the Department of Materials Science and Engineering and Prof. Ho-Young Kim from the Department of Mechanical Engineering has developed a new class of soft actuator based on elasto-magnetic instability, a mechanism that couples magnetic attraction with elastic restoring force.

The study was published in Nature Communications on January 10, 2026, under the title “Elasto-magnetic instabilities for amplified actuation and mechanical memory,” with Seong-Yu Choi and Ji-Sung Park contributing equally as co-first authors.

Engineered microorganisms are widely used in industrial biotechnology and biopharmaceutical applications, including the production of biofuels, sustainable chemicals, and therapeutic compounds. However, concerns remain regarding the unintended environmental release and uncontrolled proliferation of genetically engineered microbes. For this reason, biocontainment technologies, which are designed to prevent microorganisms from surviving outside controlled environments, have become increasingly important in both academia and industry.

Scientists have demonstrated a fiber-optic sensing method that detects strain and displacement by reading interference patterns in the electrical spectrum after photodetection. The approach uses modal delay in polymer optical fibers and may support faster, simpler sensing systems.